Stratification is a well-known approach to identify and estimate causal effects blackby blackfirst identifying blackthe underlying strata blackbefore investigating causal effects in each stratum [1]}.
The general intuition is to stratify on the confounding variable and blackto investigate the potential outcome in each stratum.
As a result, blackit becomes possible to measure the potential outcome irrespective of the confounding variable.
blackIn this situation, a blackmarginalisation over the confounding variable
can be leveraged as a combined blackestimate.
blackAs mentioned before, the hypothetical confounding variable in recommender systems is the deployed model's characteristics (Hypothesis REF ). blackDefinition  quantifies this variable as propensities. In this situation, stratification on the propensity scores allows us to analyse the effect of the deployed model's characteristics on the offline evaluation of recommendation models.
